Semi-automatic annotation. The first direct application of our strategy consists of filtering LiDAR depth maps to obtain accurate, per-pixel depth annotations. Semi-automatic processes [1]} usually rely on an external stereo setup and check for consistency between LiDAR values and disparity maps.
We compare our unsupervised model to this approach, either using a hand-crafted algorithm [2]} or state-of-the-art self-supervised stereo networks [3]}, Surface [4]} and a LiDAR-stereo fusion framework [5]}.
The comparison is performed on the 142 split since it provides manually annotated and refined ground-truth, in contrast to the CV split obtained semi-automatically. We limit to single depth map filtering and do not accumulate pointclouds over time to avoid issues with moving objects. When filtering using stereo methods, we convert LiDAR depth into disparity and filter pixels having a difference with the stereo disparity \(>1\) .
<TABLE>